Linear Mixed Effects Models for Kin & Speed Predictors for EEG

Packages & Setup

# install.packages(c("tidyverse","purrr","R.matlab","readxl","dplyr"))
#%% PACKAGES FOR STATS
library(readxl);
library(purrr);
library(tidyverse);

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tibble);
library(knitr);
library(gtsummary);

## #BlackLivesMatter

library(kableExtra);

## 
## Attaching package: 'kableExtra'
## 
## The following object is masked from 'package:dplyr':
## 
##     group_rows

library(lme4);

## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## 
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack

library(MuMIn);
library(car);

## Loading required package: carData
## 
## Attaching package: 'car'
## 
## The following object is masked from 'package:dplyr':
## 
##     recode
## 
## The following object is masked from 'package:purrr':
## 
##     some

library(effectsize);
library(sjPlot);
library(emmeans);

## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'

#%% PACKAGES FOR PLOTS & HTML HANDLING
# library(effects);
# library(sjPlot);
# library(plotly);
# library(webshot)
# library(reshape2);
# library(htmltools)
# library(Polychrome);
# library(htmlwidgets);
# library(shiny)
# library(webshot)
library(scatterplot3d)
library(RColorBrewer)
library(openxlsx)

GTSUMMARY THEME

gtsummary::set_gtsummary_theme(theme_gtsummary_journal("jama"))

## Setting theme `JAMA`
## Setting theme `JAMA`

#--
ispc <- function() {
  sys_name <- Sys.info()["sysname"]
  if (sys_name == "Windows") {
    return(TRUE)
  } else {
    return(FALSE)
  }
}

Set Parameters

#%% CLUSTERS TO PLOT
# clusters = c(3,4,6,7,8,9,10,13) # 10172024_MIM_YAOAN89_antsnorm_dipfix_iccREMG0p4_powpow0p3_skull0p01_15mmrej_speed
clusters = c(3,4,5,6,7,8,9,10,11,12,13) # RSup/RSM, PreC, LSM, Mid Cing, LSup, LPPA, RPPA
# clusters = c(3,4,5,6,7,8,9,10,11,12,13) # {'Left Sensorimotor','Right Posterior Parietal','Mid Cingulate', ...
    #'Right Cuneus','Right Sensorimotor','Left Supplementary Motor','Right Occipital', ...
    #'Left Occipital','Left Temporal','Left Posterior Parietal','Right Temporal'};
#%% EEG PARAMS
#--
eeg_measures = c('theta_avg_power','alpha_avg_power','beta_avg_power');
eeg_title_chars = c("**THETA** Mean","**ALPHA** Mean","**BETA** Mean");

Load Data

fext = 'speed_manu_tests';
excel_dir <- paste0("/jsalminen/GitHub/MIND_IN_MOTION_PRJ/MindInMotion_YoungerOlderAdults_BrainSpeedChanges/src/r_scripts/eeg_speed_lmes/fooof_spec_table.xlsx")
# excel_dir <- "/jsalminen/GitHub/MIND_IN_MOTION_PRJ/_data/MIM_dataset/_studies/02202025_mim_yaoa_powpow0p3_crit_speed/__iclabel_cluster_kmeansalt_rb3/icrej_5/11/spca_fooof_psd_anl/fooof_spec_table.xlsx"

if(ispc()){
  excel_dir <- paste0("M:",excel_dir)
}else{
  excel_dir <- paste0("/blue/dferris",excel_dir);
}

orig_eegt <- read_excel(excel_dir,sheet="Sheet1")
#%% SUBSET
orig_eegt <- orig_eegt %>%
  select(subj_char,cond_char,group_id,group_char,cluster_id,design_id,
         theta_avg_power,alpha_avg_power,beta_avg_power,
         );

Format Table

eegt <- orig_eegt;
eegt$group_name = eegt$group_char
eegt$model_char = eegt$design_id
eegt$cluster_n = eegt$cluster_id;
eegt <- filter_at(eegt,vars('cond_char'), any_vars(. %in% c('0.25','0.5','0.75','1.0')))
flat_speeds = unique(eegt$cond_char)

#%% MUTATE VARIABLES
eegt$speed_cond_num <- as.numeric(eegt$cond_char);
eegt <- mutate(eegt,across(c('subj_char'), factor))
eegt <- mutate(eegt,across(c('group_char'), factor))
eegt <- mutate(eegt,across(c('group_name'), factor))
eegt <- mutate(eegt,across(c('model_char'), factor))

#%% COLORS
color_pal_subj = brewer.pal(9,'PuBuGn')
color_pal_subj = color_pal_subj[5:9];

#%% TBL VALUES
tbl_clusterS = unique(eegt$cluster_n);
tbl_subjects = unique(eegt$subj_char);
tbl_groups = unique(eegt$group_char);

#%% DISPLAY TBL
head(eegt)

dtbl <- eegt;
rm(eegt)

Functions

calc_cohensf2 <- function(mod_main,mod_alt){
  r2_out = r.squaredGLMM(mod_main);
  r2_outalt = r.squaredGLMM(mod_alt);
  r2m = r2_out[1] # input your R2
  f2m = r2m/(1 - r2m)
  r2c = r2_out[2] # input your R2
  f2c = r2c/(1 - r2c)
  f2m = (r2_out[1]-r2_outalt[1])/(1-r2_out[1]);
  f2c = (r2_out[2]-r2_outalt[2])/(1-r2_out[2]);
  print(str_glue("r2m: {round(r2m,4)},\tr2c: {round(r2c,4)}\n\n"))
  print(str_glue("f2m: {round(f2m,4)},\tf2c: {round(f2c,4)}\n\n"))
  vals = data.frame(r2m, r2c, f2m, f2c);
  return (vals)
}

#%% EXCEL DATAFRAME
excel_df <- data.frame(cluster_num=double(),
                      group_char=character(),
                      model_char=character(),
                      kinematic_char=character(),
                      freq_band_char=character(),
                      mod_num_obs=character(),
                      coeff_chars=character(),
                      coeffs=character(),
                      confint_chars=character(),
                      emmeans=character(),
                      emmeans_se=character(),
                      confint_lwr=character(),
                      confint_upr=character(),
                      anv_chars=character(),
                      anv_pvals=character(),
                      anv_stats=character(),
                      anv_dfs=character(),
                      r2_m_int=double(),
                      r2_c_int=double(),
                      f2_m_int=double(),
                      f2_c_int=double(),
                      fsq_chars=character(),
                      fsq_vals=character(),
                      etasq_chars=character(),
                      etasq_vals=character(),
                      ran_effs_char=character(),
                      ran_effs_n=character())

SPEED-GROUP INTERACTION) LME EEG ~ 1+speed+group+speed:group

3,theta_avg_power

Changes in theta_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.55 (0.28 to 0.81)	<0.001	<0.001
speed_cond_num	0.12 (-0.04 to 0.28)	0.14	0.14
group_char		0.018	0.035
H1000’s	—
H2000’s	-0.45 (-0.85 to -0.04)
H3000’s	-0.51 (-0.89 to -0.13)
speed_cond_num * group_char		0.13	0.14
speed_cond_num * H2000’s	0.24 (0.00 to 0.48)
speed_cond_num * H3000’s	0.15 (-0.07 to 0.38)
subj_char.sd__(Intercept)	0.66 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.2621 1 5.516e-05 ** speed_cond_num 2.1691 1 0.14081
group_char 8.0703 2 0.01768
speed_cond_num:group_char 4.1239 2 0.12720
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model”

## Warning: 'r.squaredGLMM' now calculates a revised statistic. See the help page.

r2m: 0.0731, r2c: 0.8923

f2m: 0.0788, f2c: 0.1374

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

3,alpha_avg_power

Changes in alpha_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.8 (1.8 to 3.9)	<0.001	<0.001
speed_cond_num	-0.03 (-0.51 to 0.45)	0.90	0.90
group_char		0.79	0.90
H1000’s	—
H2000’s	0.56 (-1.1 to 2.2)
H3000’s	0.08 (-1.5 to 1.7)
speed_cond_num * group_char		0.37	0.74
speed_cond_num * H2000’s	-0.39 (-1.1 to 0.34)
speed_cond_num * H3000’s	-0.46 (-1.1 to 0.23)
subj_char.sd__(Intercept)	2.8 (NA to NA)
Residual.sd__Observation	0.72 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 25.9409 1 3.52e-07 *** speed_cond_num 0.0153 1 0.9016
group_char 0.4809 2 0.7863
speed_cond_num:group_char 1.9872 2 0.3702
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0063, r2c: 0.9397

f2m: 0.0063, f2c: 0.0407

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

3,beta_avg_power

Changes in beta_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.3 (1.7 to 3.0)	<0.001	<0.001
speed_cond_num	-0.24 (-0.47 to 0.00)	0.046	0.091
group_char		0.10	0.13
H1000’s	—
H2000’s	0.88 (-0.11 to 1.9)
H3000’s	0.93 (-0.01 to 1.9)
speed_cond_num * group_char		0.22	0.22
speed_cond_num * H2000’s	-0.16 (-0.52 to 0.19)
speed_cond_num * H3000’s	-0.30 (-0.64 to 0.04)
subj_char.sd__(Intercept)	1.7 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 48.9387 1 2.641e-12 ** speed_cond_num 3.9981 1 0.04555
group_char 4.6663 2 0.09699 .
speed_cond_num:group_char 3.0492 2 0.21771
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0465, r2c: 0.9611

f2m: 0.0488, f2c: 0.1552

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,theta_avg_power

Changes in theta_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.59 (0.29 to 0.89)	<0.001	<0.001
speed_cond_num	0.06 (-0.13 to 0.24)	0.53	0.71
group_char		0.17	0.34
H1000’s	—
H2000’s	-0.45 (-0.93 to 0.02)
H3000’s	-0.14 (-0.60 to 0.31)
speed_cond_num * group_char		0.77	0.77
speed_cond_num * H2000’s	0.06 (-0.23 to 0.35)
speed_cond_num * H3000’s	-0.05 (-0.33 to 0.22)
subj_char.sd__(Intercept)	0.74 (NA to NA)
Residual.sd__Observation	0.28 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 14.5013 1 0.0001401 *** speed_cond_num 0.3890 1 0.5328424
group_char 3.5262 2 0.1715120
speed_cond_num:group_char 0.5283 2 0.7678607
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0441, r2c: 0.8823

f2m: 0.0462, f2c: 0.0194

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,alpha_avg_power

Changes in alpha_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.9 (2.9 to 4.9)	<0.001	<0.001
speed_cond_num	-0.17 (-0.58 to 0.25)	0.44	0.58
group_char		0.68	0.68
H1000’s	—
H2000’s	-0.04 (-1.6 to 1.5)
H3000’s	-0.64 (-2.2 to 0.88)
speed_cond_num * group_char		0.26	0.52
speed_cond_num * H2000’s	-0.28 (-0.94 to 0.38)
speed_cond_num * H3000’s	-0.53 (-1.2 to 0.11)
subj_char.sd__(Intercept)	2.6 (NA to NA)
Residual.sd__Observation	0.63 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 58.7006 1 1.836e-14 *** speed_cond_num 0.5998 1 0.4387
group_char 0.7858 2 0.6751
speed_cond_num:group_char 2.6974 2 0.2596
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0262, r2c: 0.9457

f2m: 0.0269, f2c: 0.0724

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,beta_avg_power

Changes in beta_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.2 (1.6 to 2.8)	<0.001	<0.001
speed_cond_num	-0.13 (-0.34 to 0.07)	0.19	0.26
group_char		0.38	0.38
H1000’s	—
H2000’s	0.51 (-0.43 to 1.5)
H3000’s	0.59 (-0.32 to 1.5)
speed_cond_num * group_char		0.056	0.11
speed_cond_num * H2000’s	0.02 (-0.30 to 0.34)
speed_cond_num * H3000’s	-0.33 (-0.64 to -0.03)
subj_char.sd__(Intercept)	1.6 (NA to NA)
Residual.sd__Observation	0.31 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 50.4058 1 1.25e-12 *** speed_cond_num 1.6877 1 0.19390
group_char 1.9520 2 0.37681
speed_cond_num:group_char 5.7713 2 0.05582 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0219, r2c: 0.9644

f2m: 0.0224, f2c: 0.1057

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,theta_avg_power

Changes in theta_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.71 (0.40 to 1.0)	<0.001	<0.001
speed_cond_num	0.37 (0.17 to 0.58)	<0.001	<0.001
group_char		0.023	0.031
H1000’s	—
H2000’s	-0.35 (-0.80 to 0.10)
H3000’s	-0.62 (-1.1 to -0.18)
speed_cond_num * group_char		0.68	0.68
speed_cond_num * H2000’s	0.02 (-0.28 to 0.32)
speed_cond_num * H3000’s	0.13 (-0.17 to 0.42)
subj_char.sd__(Intercept)	0.65 (NA to NA)
Residual.sd__Observation	0.27 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 19.9026 1 8.149e-06 speed_cond_num 12.5026 1 0.0004064 group_char 7.5501 2 0.0229354 *
speed_cond_num:group_char 0.7708 2 0.6801699
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1183, r2c: 0.8683

f2m: 0.1342, f2c: 0.2763

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,alpha_avg_power

Changes in alpha_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.9 (1.2 to 2.5)	<0.001	<0.001
speed_cond_num	-0.17 (-0.55 to 0.20)	0.37	0.75
group_char		0.81	0.81
H1000’s	—
H2000’s	-0.26 (-1.2 to 0.66)
H3000’s	0.02 (-0.90 to 0.93)
speed_cond_num * group_char		0.79	0.81
speed_cond_num * H2000’s	-0.07 (-0.62 to 0.47)
speed_cond_num * H3000’s	0.12 (-0.42 to 0.65)
subj_char.sd__(Intercept)	1.4 (NA to NA)
Residual.sd__Observation	0.49 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 32.6656 1 1.095e-08 *** speed_cond_num 0.7911 1 0.3738
group_char 0.4295 2 0.8067
speed_cond_num:group_char 0.4801 2 0.7866
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0144, r2c: 0.8884

f2m: 0.0146, f2c: 0.0291

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,beta_avg_power

Changes in beta_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.91 (0.46 to 1.4)	<0.001	<0.001
speed_cond_num	-0.19 (-0.37 to -0.01)	0.038	0.076
group_char		0.15	0.20
H1000’s	—
H2000’s	0.44 (-0.22 to 1.1)
H3000’s	0.62 (-0.02 to 1.3)
speed_cond_num * group_char		0.40	0.40
speed_cond_num * H2000’s	-0.16 (-0.42 to 0.10)
speed_cond_num * H3000’s	-0.15 (-0.41 to 0.11)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 15.8035 1 7.027e-05 ** speed_cond_num 4.3124 1 0.03784
group_char 3.7849 2 0.15070
speed_cond_num:group_char 1.8483 2 0.39687
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0495, r2c: 0.951

f2m: 0.0521, f2c: 0.1902

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,theta_avg_power

Changes in theta_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.62 (0.22 to 1.0)	0.002	0.005
speed_cond_num	0.13 (-0.13 to 0.39)	0.31	0.31
group_char		<0.001	<0.001
H1000’s	—
H2000’s	0.39 (-0.19 to 0.97)
H3000’s	1.4 (0.81 to 1.9)
speed_cond_num * group_char		0.086	0.11
speed_cond_num * H2000’s	-0.02 (-0.40 to 0.36)
speed_cond_num * H3000’s	-0.37 (-0.73 to 0.00)
subj_char.sd__(Intercept)	0.94 (NA to NA)
Residual.sd__Observation	0.39 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 9.2903 1 0.002304 ** speed_cond_num 1.0219 1 0.312067
group_char 24.6432 2 4.454e-06 *** speed_cond_num:group_char 4.9017 2 0.086222 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1866, r2c: 0.884

f2m: 0.2294, f2c: 0.0265

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,alpha_avg_power

Changes in alpha_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	5.1 (4.1 to 6.0)	<0.001	<0.001
speed_cond_num	-0.26 (-0.72 to 0.19)	0.26	0.49
group_char		0.37	0.49
H1000’s	—
H2000’s	-0.84 (-2.2 to 0.56)
H3000’s	-0.86 (-2.2 to 0.49)
speed_cond_num * group_char		0.91	0.91
speed_cond_num * H2000’s	-0.01 (-0.68 to 0.65)
speed_cond_num * H3000’s	-0.13 (-0.77 to 0.50)
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.67 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 106.0081 1 <2e-16 *** speed_cond_num 1.2841 1 0.2571
group_char 1.9829 2 0.3710
speed_cond_num:group_char 0.1969 2 0.9062
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0297, r2c: 0.9304

f2m: 0.0306, f2c: 0.0356

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,beta_avg_power

Changes in beta_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.4 (1.9 to 2.9)	<0.001	<0.001
speed_cond_num	-0.14 (-0.36 to 0.08)	0.22	0.30
group_char		0.10	0.19
H1000’s	—
H2000’s	-0.29 (-0.96 to 0.38)
H3000’s	-0.71 (-1.3 to -0.06)
speed_cond_num * group_char		0.85	0.85
speed_cond_num * H2000’s	-0.07 (-0.40 to 0.25)
speed_cond_num * H3000’s	0.02 (-0.30 to 0.33)
subj_char.sd__(Intercept)	1.1 (NA to NA)
Residual.sd__Observation	0.33 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 105.6755 1 < 2e-16 *** speed_cond_num 1.4872 1 0.22265
group_char 4.6965 2 0.09554 .
speed_cond_num:group_char 0.3149 2 0.85430
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0575, r2c: 0.9277

f2m: 0.061, f2c: 0.0354

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,theta_avg_power

Changes in theta_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.68 (0.37 to 0.99)	<0.001	<0.001
speed_cond_num	0.29 (0.07 to 0.51)	0.009	0.019
group_char		0.15	0.20
H1000’s	—
H2000’s	-0.44 (-0.92 to 0.04)
H3000’s	-0.33 (-0.78 to 0.11)
speed_cond_num * group_char		0.94	0.94
speed_cond_num * H2000’s	0.01 (-0.33 to 0.35)
speed_cond_num * H3000’s	-0.04 (-0.36 to 0.27)
subj_char.sd__(Intercept)	0.66 (NA to NA)
Residual.sd__Observation	0.30 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.4751 1 1.721e-05 * speed_cond_num 6.7636 1 0.009304 group_char 3.8194 2 0.148125
speed_cond_num:group_char 0.1252 2 0.939317
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0751, r2c: 0.8415

f2m: 0.0811, f2c: 0.1051

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,alpha_avg_power

Changes in alpha_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.5 (1.5 to 3.5)	<0.001	<0.001
speed_cond_num	-0.26 (-0.65 to 0.13)	0.19	0.39
group_char		0.83	0.83
H1000’s	—
H2000’s	-0.48 (-2.0 to 1.1)
H3000’s	-0.15 (-1.6 to 1.3)
speed_cond_num * group_char		0.53	0.70
speed_cond_num * H2000’s	0.25 (-0.34 to 0.85)
speed_cond_num * H3000’s	-0.08 (-0.64 to 0.47)
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.53 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 23.9868 1 9.7e-07 *** speed_cond_num 1.6929 1 0.1932
group_char 0.3818 2 0.8262
speed_cond_num:group_char 1.2749 2 0.5286
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0039, r2c: 0.9522

f2m: 0.0039, f2c: 0.0416

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,beta_avg_power

Changes in beta_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.9 (1.1 to 2.6)	<0.001	<0.001
speed_cond_num	-0.17 (-0.43 to 0.08)	0.19	0.19
group_char		0.057	0.11
H1000’s	—
H2000’s	0.52 (-0.64 to 1.7)
H3000’s	1.3 (0.23 to 2.4)
speed_cond_num * group_char		0.10	0.13
speed_cond_num * H2000’s	-0.04 (-0.44 to 0.35)
speed_cond_num * H3000’s	-0.37 (-0.74 to -0.01)
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 23.8188 1 1.058e-06 *** speed_cond_num 1.7197 1 0.18973
group_char 5.7335 2 0.05688 .
speed_cond_num:group_char 4.6217 2 0.09918 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0623, r2c: 0.966

f2m: 0.0665, f2c: 0.1271

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,theta_avg_power

Changes in theta_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.1 (0.79 to 1.5)	<0.001	<0.001
speed_cond_num	0.22 (-0.04 to 0.48)	0.10	0.10
group_char		0.076	0.10
H1000’s	—
H2000’s	-0.39 (-0.92 to 0.14)
H3000’s	-0.59 (-1.1 to -0.07)
speed_cond_num * group_char		0.093	0.10
speed_cond_num * H2000’s	0.10 (-0.29 to 0.49)
speed_cond_num * H3000’s	0.42 (0.03 to 0.80)
subj_char.sd__(Intercept)	0.77 (NA to NA)
Residual.sd__Observation	0.37 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 40.7377 1 1.741e-10 *** speed_cond_num 2.7071 1 0.09990 .
group_char 5.1614 2 0.07572 .
speed_cond_num:group_char 4.7459 2 0.09321 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0506, r2c: 0.8226

f2m: 0.0533, f2c: 0.1495

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,alpha_avg_power

Changes in alpha_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.90 (0.46 to 1.3)	<0.001	<0.001
speed_cond_num	0.10 (-0.16 to 0.36)	0.45	0.60
group_char		0.77	0.77
H1000’s	—
H2000’s	0.13 (-0.52 to 0.78)
H3000’s	-0.12 (-0.76 to 0.52)
speed_cond_num * group_char		0.36	0.60
speed_cond_num * H2000’s	-0.05 (-0.44 to 0.35)
speed_cond_num * H3000’s	0.23 (-0.16 to 0.62)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.37 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.4763 1 4.926e-05 *** speed_cond_num 0.5693 1 0.4505
group_char 0.5292 2 0.7675
speed_cond_num:group_char 2.0604 2 0.3569
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0042, r2c: 0.8798

f2m: 0.0043, f2c: 0.0447

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,beta_avg_power

Changes in beta_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.3 (0.89 to 1.8)	<0.001	<0.001
speed_cond_num	-0.17 (-0.33 to -0.01)	0.042	0.084
group_char		0.73	0.73
H1000’s	—
H2000’s	0.23 (-0.46 to 0.91)
H3000’s	0.24 (-0.44 to 0.92)
speed_cond_num * group_char		0.50	0.67
speed_cond_num * H2000’s	-0.04 (-0.29 to 0.20)
speed_cond_num * H3000’s	0.10 (-0.14 to 0.35)
subj_char.sd__(Intercept)	1.1 (NA to NA)
Residual.sd__Observation	0.23 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 33.3222 1 7.809e-09 ** speed_cond_num 4.1275 1 0.04219
group_char 0.6329 2 0.72872
speed_cond_num:group_char 1.3675 2 0.50472
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0141, r2c: 0.9598

f2m: 0.0143, f2c: 0.0665

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,theta_avg_power

Changes in theta_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.72 (0.19 to 1.3)	0.008	0.030
speed_cond_num	0.07 (-0.21 to 0.35)	0.62	0.62
group_char		0.063	0.13
H1000’s	—
H2000’s	-0.62 (-1.4 to 0.15)
H3000’s	0.32 (-0.45 to 1.1)
speed_cond_num * group_char		0.61	0.62
speed_cond_num * H2000’s	0.19 (-0.22 to 0.60)
speed_cond_num * H3000’s	0.02 (-0.39 to 0.43)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 7.1421 1 0.007529 ** speed_cond_num 0.2523 1 0.615476
group_char 5.5227 2 0.063206 . speed_cond_num:group_char 0.9808 2 0.612367
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0938, r2c: 0.9173

f2m: 0.1035, f2c: 0.0455

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,alpha_avg_power

Changes in alpha_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.7 (2.3 to 5.1)	<0.001	<0.001
speed_cond_num	-0.16 (-0.68 to 0.35)	0.54	0.54
group_char		0.22	0.44
H1000’s	—
H2000’s	-0.74 (-2.7 to 1.3)
H3000’s	1.1 (-0.92 to 3.1)
speed_cond_num * group_char		0.34	0.46
speed_cond_num * H2000’s	-0.22 (-0.98 to 0.53)
speed_cond_num * H3000’s	-0.56 (-1.3 to 0.19)
subj_char.sd__(Intercept)	2.7 (NA to NA)
Residual.sd__Observation	0.59 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 27.9945 1 1.217e-07 *** speed_cond_num 0.3816 1 0.5367
group_char 3.0279 2 0.2200
speed_cond_num:group_char 2.1352 2 0.3438
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0541, r2c: 0.9573

f2m: 0.0572, f2c: 0.0909

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,beta_avg_power

Changes in beta_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.4 (0.87 to 1.9)	<0.001	<0.001
speed_cond_num	-0.19 (-0.45 to 0.07)	0.15	0.20
group_char		0.60	0.60
H1000’s	—
H2000’s	-0.14 (-0.91 to 0.63)
H3000’s	0.26 (-0.51 to 1.0)
speed_cond_num * group_char		0.041	0.083
speed_cond_num * H2000’s	-0.17 (-0.56 to 0.21)
speed_cond_num * H3000’s	-0.49 (-0.88 to -0.11)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.30 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 27.1407 1 1.892e-07 ** speed_cond_num 2.0601 1 0.15120
group_char 1.0383 2 0.59502
speed_cond_num:group_char 6.3718 2 0.04134
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0249, r2c: 0.9209

f2m: 0.0255, f2c: 0.2708

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,theta_avg_power

Changes in theta_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.08 (-0.29 to 0.44)	0.69	0.69
speed_cond_num	0.40 (0.14 to 0.65)	0.003	0.004
group_char		0.001	0.004
H1000’s	—
H2000’s	1.3 (0.59 to 2.0)
H3000’s	0.53 (-0.06 to 1.1)
speed_cond_num * group_char		0.003	0.004
speed_cond_num * H2000’s	-0.17 (-0.67 to 0.32)
speed_cond_num * H3000’s	0.62 (0.21 to 1.0)
subj_char.sd__(Intercept)	0.71 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 0.1619 1 0.687417
speed_cond_num 9.0591 1 0.002614 group_char 13.2771 2 0.001309 speed_cond_num:group_char 11.7551 2 0.002802 ** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.3287, r2c: 0.8879

f2m: 0.4897, f2c: 0.4365

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,alpha_avg_power

Changes in alpha_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.5 (1.6 to 3.4)	<0.001	<0.001
speed_cond_num	-0.19 (-0.64 to 0.25)	0.40	0.53
group_char		0.79	0.79
H1000’s	—
H2000’s	-0.37 (-2.0 to 1.3)
H3000’s	-0.45 (-1.8 to 0.94)
speed_cond_num * group_char		0.18	0.35
speed_cond_num * H2000’s	0.63 (-0.23 to 1.5)
speed_cond_num * H3000’s	0.58 (-0.14 to 1.3)
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.55 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 32.2899 1 1.328e-08 *** speed_cond_num 0.7059 1 0.4008
group_char 0.4610 2 0.7941
speed_cond_num:group_char 3.4828 2 0.1753
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0028, r2c: 0.914

f2m: 0.0028, f2c: 0.0637

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,beta_avg_power

Changes in beta_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.85 (0.48 to 1.2)	<0.001	<0.001
speed_cond_num	-0.13 (-0.29 to 0.03)	0.11	0.15
group_char		0.58	0.58
H1000’s	—
H2000’s	-0.22 (-0.93 to 0.50)
H3000’s	-0.31 (-0.90 to 0.29)
speed_cond_num * group_char		0.10	0.15
speed_cond_num * H2000’s	0.34 (0.02 to 0.65)
speed_cond_num * H3000’s	0.15 (-0.11 to 0.41)
subj_char.sd__(Intercept)	0.79 (NA to NA)
Residual.sd__Observation	0.20 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 20.2703 1 6.724e-06 *** speed_cond_num 2.5618 1 0.10948
group_char 1.1013 2 0.57657
speed_cond_num:group_char 4.6616 2 0.09722 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0165, r2c: 0.9394

f2m: 0.0168, f2c: 0.0712

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,theta_avg_power

Changes in theta_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.75 (0.39 to 1.1)	<0.001	<0.001
speed_cond_num	-0.01 (-0.28 to 0.25)	0.92	0.92
group_char		0.77	0.92
H1000’s	—
H2000’s	-0.21 (-0.77 to 0.35)
H3000’s	-0.09 (-0.59 to 0.41)
speed_cond_num * group_char		0.074	0.15
speed_cond_num * H2000’s	0.46 (0.06 to 0.87)
speed_cond_num * H3000’s	0.13 (-0.24 to 0.49)
subj_char.sd__(Intercept)	0.68 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.3745 1 5.198e-05 *** speed_cond_num 0.0108 1 0.91719
group_char 0.5277 2 0.76808
speed_cond_num:group_char 5.2205 2 0.07352 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0103, r2c: 0.824

f2m: 0.0104, f2c: 0.0788

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,alpha_avg_power

Changes in alpha_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.1 (1.1 to 3.1)	<0.001	<0.001
speed_cond_num	-0.30 (-0.70 to 0.10)	0.15	0.19
group_char		0.025	0.050
H1000’s	—
H2000’s	1.9 (0.39 to 3.4)
H3000’s	1.5 (0.14 to 2.8)
speed_cond_num * group_char		0.96	0.96
speed_cond_num * H2000’s	-0.01 (-0.63 to 0.61)
speed_cond_num * H3000’s	-0.07 (-0.63 to 0.48)
subj_char.sd__(Intercept)	2.0 (NA to NA)
Residual.sd__Observation	0.49 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.1307 1 2.062e-05 ** speed_cond_num 2.1156 1 0.14581
group_char 7.3875 2 0.02488
speed_cond_num:group_char 0.0764 2 0.96251
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1286, r2c: 0.9518

f2m: 0.1476, f2c: 0.0629

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,beta_avg_power

Changes in beta_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.0 (0.58 to 1.4)	<0.001	<0.001
speed_cond_num	-0.21 (-0.41 to -0.01)	0.044	0.087
group_char		0.95	0.95
H1000’s	—
H2000’s	0.08 (-0.57 to 0.74)
H3000’s	-0.03 (-0.61 to 0.56)
speed_cond_num * group_char		0.45	0.60
speed_cond_num * H2000’s	-0.04 (-0.35 to 0.27)
speed_cond_num * H3000’s	-0.17 (-0.45 to 0.11)
subj_char.sd__(Intercept)	0.87 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 21.3393 1 3.848e-06 ** speed_cond_num 4.0666 1 0.04374
group_char 0.1108 2 0.94610
speed_cond_num:group_char 1.5971 2 0.44997
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0157, r2c: 0.9293

f2m: 0.016, f2c: 0.1783

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,theta_avg_power

Changes in theta_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.52 (0.18 to 0.86)	0.003	0.012
speed_cond_num	0.07 (-0.09 to 0.23)	0.41	0.50
group_char		0.50	0.50
H1000’s	—
H2000’s	-0.26 (-0.79 to 0.27)
H3000’s	0.05 (-0.46 to 0.56)
speed_cond_num * group_char		0.045	0.089
speed_cond_num * H2000’s	0.30 (0.04 to 0.55)
speed_cond_num * H3000’s	0.24 (-0.01 to 0.48)
subj_char.sd__(Intercept)	0.87 (NA to NA)
Residual.sd__Observation	0.25 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 8.8009 1 0.003011 ** speed_cond_num 0.6908 1 0.405877
group_char 1.3827 2 0.500889
speed_cond_num:group_char 6.2166 2 0.044677 * — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0214, r2c: 0.9263

f2m: 0.0219, f2c: 0.1351

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,alpha_avg_power

Changes in alpha_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	4.3 (3.3 to 5.2)	<0.001	<0.001
speed_cond_num	-0.31 (-0.68 to 0.06)	0.10	0.21
group_char		0.45	0.56
H1000’s	—
H2000’s	-0.38 (-1.8 to 1.1)
H3000’s	-0.90 (-2.3 to 0.50)
speed_cond_num * group_char		0.56	0.56
speed_cond_num * H2000’s	-0.01 (-0.59 to 0.57)
speed_cond_num * H3000’s	0.27 (-0.29 to 0.83)
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.57 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 79.1451 1 <2e-16 *** speed_cond_num 2.6341 1 0.1046
group_char 1.5873 2 0.4522
speed_cond_num:group_char 1.1710 2 0.5568
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0158, r2c: 0.9496

f2m: 0.0161, f2c: 0.0365

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,beta_avg_power

Changes in beta_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.2 (1.7 to 2.7)	<0.001	<0.001
speed_cond_num	-0.08 (-0.31 to 0.15)	0.50	0.67
group_char		0.94	0.94
H1000’s	—
H2000’s	0.14 (-0.65 to 0.93)
H3000’s	0.04 (-0.72 to 0.80)
speed_cond_num * group_char		0.17	0.34
speed_cond_num * H2000’s	-0.34 (-0.70 to 0.02)
speed_cond_num * H3000’s	-0.16 (-0.51 to 0.18)
subj_char.sd__(Intercept)	1.3 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 72.6717 1 <2e-16 *** speed_cond_num 0.4522 1 0.5013
group_char 0.1234 2 0.9402
speed_cond_num:group_char 3.5218 2 0.1719
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0037, r2c: 0.9337

f2m: 0.0037, f2c: 0.077

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,theta_avg_power

Changes in theta_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.51 (0.03 to 0.99)	0.036	0.076
speed_cond_num	0.29 (0.02 to 0.56)	0.038	0.076
group_char		0.74	0.74
H1000’s	—
H2000’s	0.27 (-0.46 to 0.99)
H3000’s	0.03 (-0.62 to 0.68)
speed_cond_num * group_char		0.44	0.59
speed_cond_num * H2000’s	-0.17 (-0.58 to 0.25)
speed_cond_num * H3000’s	0.09 (-0.28 to 0.47)
subj_char.sd__(Intercept)	0.81 (NA to NA)
Residual.sd__Observation	0.28 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 4.3913 1 0.03612 speed_cond_num 4.3047 1 0.03801 group_char 0.6109 2 0.73681
speed_cond_num:group_char 1.6212 2 0.44458
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0149, r2c: 0.8941

f2m: 0.0151, f2c: 0.1518

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,alpha_avg_power

Changes in alpha_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.78 (-0.21 to 1.8)	0.12	0.25
speed_cond_num	0.10 (-0.28 to 0.48)	0.60	0.60
group_char		0.048	0.19
H1000’s	—
H2000’s	1.7 (0.23 to 3.2)
H3000’s	1.4 (0.00 to 2.7)
speed_cond_num * group_char		0.37	0.49
speed_cond_num * H2000’s	-0.29 (-0.87 to 0.29)
speed_cond_num * H3000’s	-0.36 (-0.89 to 0.16)
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.39 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 2.3681 1 0.12383
speed_cond_num 0.2698 1 0.60346
group_char 6.0790 2 0.04786 * speed_cond_num:group_char 1.9850 2 0.37065
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1126, r2c: 0.9576

f2m: 0.1269, f2c: 0.0474

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,beta_avg_power

Changes in beta_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.84 (0.40 to 1.3)	<0.001	<0.001
speed_cond_num	-0.10 (-0.31 to 0.11)	0.35	0.48
group_char		0.36	0.48
H1000’s	—
H2000’s	0.09 (-0.57 to 0.75)
H3000’s	-0.34 (-0.93 to 0.26)
speed_cond_num * group_char		0.83	0.83
speed_cond_num * H2000’s	-0.10 (-0.42 to 0.22)
speed_cond_num * H3000’s	-0.03 (-0.32 to 0.26)
subj_char.sd__(Intercept)	0.76 (NA to NA)
Residual.sd__Observation	0.22 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 14.2172 1 0.0001629 *** speed_cond_num 0.8662 1 0.3520098
group_char 2.0633 2 0.3564231
speed_cond_num:group_char 0.3741 2 0.8294066
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0527, r2c: 0.9283

f2m: 0.0557, f2c: 0.072

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

SPEED-GROUP MAIN) LME EEG ~ 1+speed+group

3,theta_avg_power

Changes in theta_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.47 (0.22 to 0.72)	<0.001	<0.001
speed_cond_num	0.24 (0.14 to 0.34)	<0.001	<0.001
group_char		0.062	0.062
H1000’s	—
H2000’s	-0.30 (-0.67 to 0.08)
H3000’s	-0.42 (-0.77 to -0.06)
subj_char.sd__(Intercept)	0.66 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 13.1923 1 0.0002811 speed_cond_num 23.6036 1 1.184e-06 group_char 5.5658 2 0.0618576 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0716, r2c: 0.8912

f2m: 0.0772, f2c: 0.1259

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

3,alpha_avg_power

Changes in alpha_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.0 (1.9 to 4.1)	<0.001	<0.001
speed_cond_num	-0.30 (-0.59 to -0.01)	0.044	0.066
group_char		0.82	0.82
H1000’s	—
H2000’s	0.32 (-1.3 to 1.9)
H3000’s	-0.21 (-1.7 to 1.3)
subj_char.sd__(Intercept)	2.8 (NA to NA)
Residual.sd__Observation	0.72 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 30.4952 1 3.347e-08 ** speed_cond_num 4.0468 1 0.04425
group_char 0.3996 2 0.81890
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0059, r2c: 0.9396

f2m: 0.0059, f2c: 0.0404

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

3,beta_avg_power

Changes in beta_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.4 (1.8 to 3.1)	<0.001	<0.001
speed_cond_num	-0.39 (-0.53 to -0.25)	<0.001	<0.001
group_char		0.18	0.18
H1000’s	—
H2000’s	0.78 (-0.19 to 1.8)
H3000’s	0.74 (-0.18 to 1.7)
subj_char.sd__(Intercept)	1.7 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 54.6565 1 1.435e-13 speed_cond_num 28.2573 1 1.062e-07 group_char 3.4298 2 0.18
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0461, r2c: 0.9609

f2m: 0.0483, f2c: 0.1495

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,theta_avg_power

Changes in theta_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.59 (0.30 to 0.88)	<0.001	<0.001
speed_cond_num	0.06 (-0.06 to 0.17)	0.33	0.33
group_char		0.18	0.27
H1000’s	—
H2000’s	-0.42 (-0.86 to 0.02)
H3000’s	-0.18 (-0.60 to 0.24)
subj_char.sd__(Intercept)	0.74 (NA to NA)
Residual.sd__Observation	0.28 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 15.9077 1 6.651e-05 *** speed_cond_num 0.9343 1 0.3338
group_char 3.4467 2 0.1785
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0439, r2c: 0.8831

f2m: 0.0459, f2c: 0.0258

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,alpha_avg_power

Changes in alpha_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	4.1 (3.1 to 5.1)	<0.001	<0.001
speed_cond_num	-0.41 (-0.68 to -0.14)	0.003	0.004
group_char		0.41	0.41
H1000’s	—
H2000’s	-0.22 (-1.7 to 1.3)
H3000’s	-0.96 (-2.4 to 0.50)
subj_char.sd__(Intercept)	2.6 (NA to NA)
Residual.sd__Observation	0.63 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 66.0381 1 4.423e-16 * speed_cond_num 9.0987 1 0.002558 group_char 1.7591 2 0.414976
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0257, r2c: 0.9455

f2m: 0.0264, f2c: 0.0684

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,beta_avg_power

Changes in beta_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.2 (1.6 to 2.8)	<0.001	<0.001
speed_cond_num	-0.24 (-0.37 to -0.10)	<0.001	<0.001
group_char		0.49	0.49
H1000’s	—
H2000’s	0.53 (-0.40 to 1.5)
H3000’s	0.38 (-0.51 to 1.3)
subj_char.sd__(Intercept)	1.6 (NA to NA)
Residual.sd__Observation	0.31 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 54.7924 1 1.34e-13 speed_cond_num 12.4383 1 0.0004206 group_char 1.4071 2 0.4948224
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0211, r2c: 0.9637

f2m: 0.0216, f2c: 0.0856

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,theta_avg_power

Changes in theta_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.68 (0.38 to 0.97)	<0.001	<0.001
speed_cond_num	0.42 (0.30 to 0.55)	<0.001	<0.001
group_char		0.029	0.029
H1000’s	—
H2000’s	-0.34 (-0.75 to 0.07)
H3000’s	-0.54 (-0.95 to -0.14)
subj_char.sd__(Intercept)	0.65 (NA to NA)
Residual.sd__Observation	0.27 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 20.539 1 5.844e-06 speed_cond_num 46.049 1 1.153e-11 group_char 7.075 2 0.02909 *
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.118, r2c: 0.8691

f2m: 0.1338, f2c: 0.2837

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,alpha_avg_power

Changes in alpha_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.9 (1.2 to 2.5)	<0.001	<0.001
speed_cond_num	-0.15 (-0.38 to 0.07)	0.17	0.25
group_char		0.64	0.64
H1000’s	—
H2000’s	-0.31 (-1.2 to 0.55)
H3000’s	0.09 (-0.76 to 0.94)
subj_char.sd__(Intercept)	1.4 (NA to NA)
Residual.sd__Observation	0.49 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 35.4282 1 2.646e-09 *** speed_cond_num 1.8843 1 0.1698
group_char 0.8828 2 0.6431
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0142, r2c: 0.8892

f2m: 0.0144, f2c: 0.0369

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,beta_avg_power

Changes in beta_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.97 (0.53 to 1.4)	<0.001	<0.001
speed_cond_num	-0.29 (-0.40 to -0.18)	<0.001	<0.001
group_char		0.24	0.24
H1000’s	—
H2000’s	0.34 (-0.29 to 0.97)
H3000’s	0.53 (-0.10 to 1.1)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.8268 1 1.431e-05 speed_cond_num 28.7503 1 8.234e-08 group_char 2.8305 2 0.2429
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0492, r2c: 0.9511

f2m: 0.0517, f2c: 0.1907

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,theta_avg_power

Changes in theta_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.70 (0.33 to 1.1)	<0.001	<0.001
speed_cond_num	0.00 (-0.16 to 0.15)	0.97	0.97
group_char		<0.001	<0.001
H1000’s	—
H2000’s	0.37 (-0.16 to 0.90)
H3000’s	1.1 (0.63 to 1.7)
subj_char.sd__(Intercept)	0.94 (NA to NA)
Residual.sd__Observation	0.39 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 13.5089 1 0.0002374 speed_cond_num 0.0015 1 0.9691485
group_char 20.0355 2 4.46e-05 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1849, r2c: 0.8825

f2m: 0.2269, f2c: 0.0132

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,alpha_avg_power

Changes in alpha_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	5.1 (4.2 to 6.0)	<0.001	<0.001
speed_cond_num	-0.31 (-0.58 to -0.05)	0.020	0.030
group_char		0.29	0.29
H1000’s	—
H2000’s	-0.85 (-2.2 to 0.49)
H3000’s	-0.95 (-2.2 to 0.35)
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.67 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 113.8624 1 < 2e-16 ** speed_cond_num 5.4069 1 0.02006
group_char 2.4428 2 0.29482
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0297, r2c: 0.9309

f2m: 0.0306, f2c: 0.0431

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,beta_avg_power

Changes in beta_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.4 (2.0 to 2.9)	<0.001	<0.001
speed_cond_num	-0.16 (-0.28 to -0.03)	0.019	0.029
group_char		0.083	0.083
H1000’s	—
H2000’s	-0.34 (-0.97 to 0.30)
H3000’s	-0.70 (-1.3 to -0.08)
subj_char.sd__(Intercept)	1.1 (NA to NA)
Residual.sd__Observation	0.33 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 113.5078 1 < 2e-16 ** speed_cond_num 5.4825 1 0.01921
group_char 4.9820 2 0.08283 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0575, r2c: 0.9282

f2m: 0.061, f2c: 0.0423

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,theta_avg_power

Changes in theta_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.69 (0.40 to 0.98)	<0.001	<0.001
speed_cond_num	0.28 (0.15 to 0.41)	<0.001	<0.001
group_char		0.085	0.085
H1000’s	—
H2000’s	-0.43 (-0.86 to -0.01)
H3000’s	-0.36 (-0.76 to 0.04)
subj_char.sd__(Intercept)	0.66 (NA to NA)
Residual.sd__Observation	0.30 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 21.5834 1 3.388e-06 speed_cond_num 16.9237 1 3.891e-05 group_char 4.9197 2 0.08545 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0751, r2c: 0.8429

f2m: 0.0812, f2c: 0.115

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,alpha_avg_power

Changes in alpha_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.5 (1.5 to 3.5)	<0.001	<0.001
speed_cond_num	-0.22 (-0.45 to 0.02)	0.070	0.11
group_char		0.91	0.91
H1000’s	—
H2000’s	-0.32 (-1.8 to 1.2)
H3000’s	-0.21 (-1.6 to 1.2)
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.53 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 24.4131 1 7.774e-07 *** speed_cond_num 3.2714 1 0.0705 .
group_char 0.1914 2 0.9088
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0036, r2c: 0.9523

f2m: 0.0036, f2c: 0.0453

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,beta_avg_power

Changes in beta_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.0 (1.2 to 2.7)	<0.001	<0.001
speed_cond_num	-0.32 (-0.47 to -0.16)	<0.001	<0.001
group_char		0.13	0.13
H1000’s	—
H2000’s	0.50 (-0.64 to 1.6)
H3000’s	1.1 (0.02 to 2.1)
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 26.9043 1 2.138e-07 speed_cond_num 15.5857 1 7.885e-05 group_char 4.0288 2 0.1334
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0617, r2c: 0.9656

f2m: 0.0658, f2c: 0.111

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,theta_avg_power

Changes in theta_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.0 (0.71 to 1.4)	<0.001	<0.001
speed_cond_num	0.38 (0.22 to 0.54)	<0.001	<0.001
group_char		0.27	0.27
H1000’s	—
H2000’s	-0.33 (-0.79 to 0.14)
H3000’s	-0.33 (-0.79 to 0.13)
subj_char.sd__(Intercept)	0.77 (NA to NA)
Residual.sd__Observation	0.37 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 38.9553 1 4.336e-10 speed_cond_num 21.4540 1 3.624e-06 group_char 2.6385 2 0.2673
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0475, r2c: 0.8199

f2m: 0.0499, f2c: 0.1322

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,alpha_avg_power

Changes in alpha_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.86 (0.45 to 1.3)	<0.001	<0.001
speed_cond_num	0.16 (0.00 to 0.32)	0.053	0.079
group_char		0.94	0.94
H1000’s	—
H2000’s	0.10 (-0.50 to 0.70)
H3000’s	0.02 (-0.57 to 0.62)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.37 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.6351 1 4.53e-05 *** speed_cond_num 3.7470 1 0.0529 .
group_char 0.1155 2 0.9439
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0033, r2c: 0.8797

f2m: 0.0033, f2c: 0.0434

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,beta_avg_power

Changes in beta_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.3 (0.88 to 1.8)	<0.001	<0.001
speed_cond_num	-0.15 (-0.25 to -0.05)	0.003	0.005
group_char		0.65	0.65
H1000’s	—
H2000’s	0.20 (-0.47 to 0.87)
H3000’s	0.30 (-0.35 to 0.96)
subj_char.sd__(Intercept)	1.1 (NA to NA)
Residual.sd__Observation	0.23 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 33.7704 1 6.201e-09 * speed_cond_num 8.5527 1 0.00345 group_char 0.8653 2 0.64879
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0139, r2c: 0.9599

f2m: 0.0141, f2c: 0.0696

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,theta_avg_power

Changes in theta_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.68 (0.17 to 1.2)	0.009	0.027
speed_cond_num	0.14 (-0.03 to 0.31)	0.10	0.10
group_char		0.092	0.10
H1000’s	—
H2000’s	-0.50 (-1.2 to 0.23)
H3000’s	0.34 (-0.40 to 1.1)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 6.8021 1 0.009105 ** speed_cond_num 2.6484 1 0.103654
group_char 4.7702 2 0.092078 . — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0934, r2c: 0.9178

f2m: 0.103, f2c: 0.0527

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,alpha_avg_power

Changes in alpha_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.8 (2.5 to 5.2)	<0.001	<0.001
speed_cond_num	-0.41 (-0.72 to -0.10)	0.009	0.014
group_char		0.29	0.29
H1000’s	—
H2000’s	-0.88 (-2.8 to 1.1)
H3000’s	0.74 (-1.2 to 2.7)
subj_char.sd__(Intercept)	2.7 (NA to NA)
Residual.sd__Observation	0.59 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 31.5139 1 1.98e-08 * speed_cond_num 6.7540 1 0.009354 group_char 2.4846 2 0.288713
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0536, r2c: 0.9572

f2m: 0.0566, f2c: 0.0892

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,beta_avg_power

Changes in beta_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.5 (1.0 to 2.0)	<0.001	<0.001
speed_cond_num	-0.40 (-0.57 to -0.24)	<0.001	<0.001
group_char		0.77	0.77
H1000’s	—
H2000’s	-0.25 (-0.98 to 0.48)
H3000’s	-0.04 (-0.78 to 0.69)
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.30 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 34.6336 1 3.980e-09 speed_cond_num 24.2111 1 8.634e-07 group_char 0.5121 2 0.7741
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.022, r2c: 0.9182

f2m: 0.0225, f2c: 0.2286

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,theta_avg_power

Changes in theta_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	-0.03 (-0.38 to 0.32)	0.88	0.88
speed_cond_num	0.56 (0.37 to 0.75)	<0.001	<0.001
group_char		<0.001	<0.001
H1000’s	—
H2000’s	1.2 (0.55 to 1.8)
H3000’s	0.92 (0.39 to 1.4)
subj_char.sd__(Intercept)	0.71 (NA to NA)
Residual.sd__Observation	0.33 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 0.0242 1 0.8764
speed_cond_num 33.4908 1 7.160e-09 group_char 18.8377 2 8.118e-05 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.3205, r2c: 0.8781

f2m: 0.4717, f2c: 0.3204

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,alpha_avg_power

Changes in alpha_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.3 (1.5 to 3.2)	<0.001	<0.001
speed_cond_num	0.11 (-0.21 to 0.42)	0.50	0.75
group_char		0.99	0.99
H1000’s	—
H2000’s	0.02 (-1.6 to 1.6)
H3000’s	-0.09 (-1.4 to 1.2)
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.56 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 29.1430 1 6.723e-08 *** speed_cond_num 0.4491 1 0.5028
group_char 0.0227 2 0.9887
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 8e-04, r2c: 0.9128

f2m: 8e-04, f2c: 0.0487

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,beta_avg_power

Changes in beta_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.78 (0.42 to 1.1)	<0.001	<0.001
speed_cond_num	-0.02 (-0.14 to 0.09)	0.70	0.75
group_char		0.75	0.75
H1000’s	—
H2000’s	-0.01 (-0.69 to 0.68)
H3000’s	-0.21 (-0.79 to 0.36)
subj_char.sd__(Intercept)	0.79 (NA to NA)
Residual.sd__Observation	0.20 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 17.7986 1 2.456e-05 *** speed_cond_num 0.1527 1 0.6959
group_char 0.5852 2 0.7463
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0146, r2c: 0.9379

f2m: 0.0148, f2c: 0.0458

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,theta_avg_power

Changes in theta_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.64 (0.31 to 0.98)	<0.001	<0.001
speed_cond_num	0.15 (0.00 to 0.31)	0.056	0.083
group_char		0.93	0.93
H1000’s	—
H2000’s	0.08 (-0.42 to 0.58)
H3000’s	-0.01 (-0.45 to 0.44)
subj_char.sd__(Intercept)	0.68 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 13.9319 1 0.0001895 *** speed_cond_num 3.6638 1 0.0556083 .
group_char 0.1487 2 0.9283667
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0058, r2c: 0.8199

f2m: 0.0058, f2c: 0.0546

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,alpha_avg_power

Changes in alpha_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.1 (1.2 to 3.1)	<0.001	<0.001
speed_cond_num	-0.33 (-0.57 to -0.09)	0.007	0.010
group_char		0.021	0.021
H1000’s	—
H2000’s	1.9 (0.43 to 3.3)
H3000’s	1.4 (0.14 to 2.7)
subj_char.sd__(Intercept)	2.0 (NA to NA)
Residual.sd__Observation	0.49 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 19.3132 1 1.109e-05 speed_cond_num 7.4046 1 0.006506 group_char 7.7061 2 0.021214
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1286, r2c: 0.9524

f2m: 0.1476, f2c: 0.076

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,beta_avg_power

Changes in beta_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.0 (0.64 to 1.5)	<0.001	<0.001
speed_cond_num	-0.28 (-0.40 to -0.16)	<0.001	<0.001
group_char		0.81	0.81
H1000’s	—
H2000’s	0.06 (-0.57 to 0.68)
H3000’s	-0.13 (-0.69 to 0.43)
subj_char.sd__(Intercept)	0.87 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 24.8404 1 6.228e-07 speed_cond_num 21.8450 1 2.956e-06 group_char 0.4138 2 0.8131
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0152, r2c: 0.9295

f2m: 0.0154, f2c: 0.1806

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,theta_avg_power

Changes in theta_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.42 (0.08 to 0.75)	0.014	0.021
speed_cond_num	0.23 (0.13 to 0.33)	<0.001	<0.001
group_char		0.57	0.57
H1000’s	—
H2000’s	-0.07 (-0.58 to 0.43)
H3000’s	0.20 (-0.29 to 0.68)
subj_char.sd__(Intercept)	0.87 (NA to NA)
Residual.sd__Observation	0.25 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 6.0492 1 0.01391 *
speed_cond_num 18.6504 1 1.57e-05 *** group_char 1.1359 2 0.56668
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0198, r2c: 0.9247

f2m: 0.0202, f2c: 0.1124

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,alpha_avg_power

Changes in alpha_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	4.2 (3.3 to 5.1)	<0.001	<0.001
speed_cond_num	-0.23 (-0.46 to 0.01)	0.059	0.089
group_char		0.57	0.57
H1000’s	—
H2000’s	-0.39 (-1.8 to 1.0)
H3000’s	-0.73 (-2.1 to 0.62)
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.56 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.2372 1 < 2e-16 *** speed_cond_num 3.5566 1 0.05931 .
group_char 1.1213 2 0.57083
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0156, r2c: 0.9498

f2m: 0.0159, f2c: 0.0402

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,beta_avg_power

Changes in beta_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.3 (1.8 to 2.8)	<0.001	<0.001
speed_cond_num	-0.23 (-0.38 to -0.08)	0.002	0.003
group_char		0.98	0.98
H1000’s	—
H2000’s	-0.07 (-0.84 to 0.69)
H3000’s	-0.06 (-0.79 to 0.67)
subj_char.sd__(Intercept)	1.3 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 82.9493 1 < 2.2e-16 * speed_cond_num 9.4525 1 0.002109 group_char 0.0460 2 0.977258
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0028, r2c: 0.9332

f2m: 0.0029, f2c: 0.0689

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,theta_avg_power

Changes in theta_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.51 (0.06 to 0.97)	0.027	0.041
speed_cond_num	0.28 (0.12 to 0.44)	<0.001	0.002
group_char		0.89	0.89
H1000’s	—
H2000’s	0.16 (-0.51 to 0.84)
H3000’s	0.09 (-0.52 to 0.69)
subj_char.sd__(Intercept)	0.81 (NA to NA)
Residual.sd__Observation	0.28 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 4.8762 1 0.0272294 *
speed_cond_num 12.0244 1 0.0005251 *** group_char 0.2240 2 0.8940312
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0138, r2c: 0.8943

f2m: 0.014, f2c: 0.154

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,alpha_avg_power

Changes in alpha_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.92 (-0.06 to 1.9)	0.065	0.13
speed_cond_num	-0.12 (-0.34 to 0.11)	0.30	0.30
group_char		0.085	0.13
H1000’s	—
H2000’s	1.6 (0.09 to 3.0)
H3000’s	1.1 (-0.19 to 2.4)
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.39 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 3.4075 1 0.06490 . speed_cond_num 1.0684 1 0.30132
group_char 4.9318 2 0.08493 . — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.1121, r2c: 0.9576

f2m: 0.1263, f2c: 0.047

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,beta_avg_power

Changes in beta_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.86 (0.44 to 1.3)	<0.001	<0.001
speed_cond_num	-0.14 (-0.26 to -0.02)	0.026	0.039
group_char		0.35	0.35
H1000’s	—
H2000’s	0.03 (-0.60 to 0.66)
H3000’s	-0.36 (-0.92 to 0.21)
subj_char.sd__(Intercept)	0.76 (NA to NA)
Residual.sd__Observation	0.22 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.0226 1 6.259e-05 ** speed_cond_num 4.9565 1 0.02599
group_char 2.1220 2 0.34612
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0526, r2c: 0.9292

f2m: 0.0555, f2c: 0.0867

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

EEG-SPEED ALL) LME EEG ~ 1+speed

3,theta_avg_power

Changes in theta_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.24 (0.08 to 0.41)	0.004	0.004
speed_cond_num	0.24 (0.14 to 0.34)	<0.001	<0.001
subj_char.sd__(Intercept)	0.67 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 8.2907 1 0.003985 ** speed_cond_num 23.6036 1 1.184e-06 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0088, r2c: 0.8888

f2m: 0.0089, f2c: 0.1009

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

3,alpha_avg_power

Changes in alpha_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.0 (2.4 to 3.7)	<0.001	<0.001
speed_cond_num	-0.30 (-0.59 to -0.01)	0.044	0.044
subj_char.sd__(Intercept)	2.8 (NA to NA)
Residual.sd__Observation	0.72 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.3627 1 < 2e-16 ** speed_cond_num 4.0468 1 0.04425
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 8e-04, r2c: 0.9381

f2m: 8e-04, f2c: 0.0138

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

3,beta_avg_power

Changes in beta_avg_power for Cluster: 3
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.9 (2.5 to 3.3)	<0.001	<0.001
speed_cond_num	-0.39 (-0.53 to -0.25)	<0.001	<0.001
subj_char.sd__(Intercept)	1.7 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 199.234 1 < 2.2e-16 speed_cond_num 28.257 1 1.062e-07 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0038, r2c: 0.9599

f2m: 0.0038, f2c: 0.1213

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,theta_avg_power

Changes in theta_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.42 (0.22 to 0.61)	<0.001	<0.001
speed_cond_num	0.06 (-0.06 to 0.17)	0.33	0.33
subj_char.sd__(Intercept)	0.75 (NA to NA)
Residual.sd__Observation	0.28 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 17.6849 1 2.607e-05 *** speed_cond_num 0.9343 1 0.3338
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 4e-04, r2c: 0.8801

f2m: 4e-04, f2c: 1e-04

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,alpha_avg_power

Changes in alpha_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.7 (3.1 to 4.4)	<0.001	<0.001
speed_cond_num	-0.41 (-0.68 to -0.14)	0.003	0.003
subj_char.sd__(Intercept)	2.6 (NA to NA)
Residual.sd__Observation	0.63 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 129.7694 1 < 2.2e-16 * speed_cond_num 9.0987 1 0.002558 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0019, r2c: 0.9439

f2m: 0.0019, f2c: 0.0393

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

4,beta_avg_power

Changes in beta_avg_power for Cluster: 4
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.5 (2.1 to 2.9)	<0.001	<0.001
speed_cond_num	-0.24 (-0.37 to -0.10)	<0.001	<0.001
subj_char.sd__(Intercept)	1.6 (NA to NA)
Residual.sd__Observation	0.31 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 165.759 1 < 2.2e-16 speed_cond_num 12.438 1 0.0004206 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0017, r2c: 0.9627

f2m: 0.0017, f2c: 0.0554

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,theta_avg_power

Changes in theta_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.39 (0.20 to 0.58)	<0.001	<0.001
speed_cond_num	0.42 (0.30 to 0.55)	<0.001	<0.001
subj_char.sd__(Intercept)	0.68 (NA to NA)
Residual.sd__Observation	0.27 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 16.078 1 6.080e-05 speed_cond_num 46.049 1 1.153e-11 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0259, r2c: 0.8657

f2m: 0.0266, f2c: 0.2511

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,alpha_avg_power

Changes in alpha_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.8 (1.4 to 2.2)	<0.001	<0.001
speed_cond_num	-0.15 (-0.38 to 0.07)	0.17	0.17
subj_char.sd__(Intercept)	1.4 (NA to NA)
Residual.sd__Observation	0.49 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 87.1467 1 <2e-16 *** speed_cond_num 1.8843 1 0.1698
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 9e-04, r2c: 0.8857

f2m: 9e-04, f2c: 0.0054

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

5,beta_avg_power

Changes in beta_avg_power for Cluster: 5
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.3 (0.99 to 1.5)	<0.001	<0.001
speed_cond_num	-0.29 (-0.40 to -0.18)	<0.001	<0.001
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 84.714 1 < 2.2e-16 speed_cond_num 28.750 1 8.234e-08 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0061, r2c: 0.9495

f2m: 0.0061, f2c: 0.1544

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,theta_avg_power

Changes in theta_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.2 (0.97 to 1.5)	<0.001	<0.001
speed_cond_num	0.00 (-0.16 to 0.15)	0.97	0.97
subj_char.sd__(Intercept)	1.1 (NA to NA)
Residual.sd__Observation	0.39 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 88.7238 1 <2e-16 *** speed_cond_num 0.0015 1 0.9691
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0, r2c: 0.8805

f2m: 0, f2c: -0.0038

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,alpha_avg_power

Changes in alpha_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	4.5 (3.9 to 5.1)	<0.001	<0.001
speed_cond_num	-0.31 (-0.58 to -0.05)	0.020	0.020
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.67 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 244.0644 1 < 2e-16 ** speed_cond_num 5.4069 1 0.02006
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0012, r2c: 0.9293

f2m: 0.0012, f2c: 0.0188

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

6,beta_avg_power

Changes in beta_avg_power for Cluster: 6
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.1 (1.8 to 2.3)	<0.001	<0.001
speed_cond_num	-0.16 (-0.28 to -0.03)	0.019	0.019
subj_char.sd__(Intercept)	1.2 (NA to NA)
Residual.sd__Observation	0.33 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 220.6644 1 < 2e-16 ** speed_cond_num 5.4825 1 0.01921
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0013, r2c: 0.9265

f2m: 0.0013, f2c: 0.0192

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,theta_avg_power

Changes in theta_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.44 (0.25 to 0.63)	<0.001	<0.001
speed_cond_num	0.28 (0.15 to 0.41)	<0.001	<0.001
subj_char.sd__(Intercept)	0.68 (NA to NA)
Residual.sd__Observation	0.30 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 20.194 1 6.997e-06 speed_cond_num 16.924 1 3.891e-05 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.011, r2c: 0.8388

f2m: 0.0112, f2c: 0.0864

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,alpha_avg_power

Changes in alpha_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.3 (1.7 to 2.9)	<0.001	<0.001
speed_cond_num	-0.22 (-0.45 to 0.02)	0.070	0.070
subj_char.sd__(Intercept)	2.3 (NA to NA)
Residual.sd__Observation	0.53 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 56.9891 1 4.382e-14 *** speed_cond_num 3.2714 1 0.0705 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 7e-04, r2c: 0.9508

f2m: 7e-04, f2c: 0.0124

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

7,beta_avg_power

Changes in beta_avg_power for Cluster: 7
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.5 (2.0 to 3.0)	<0.001	<0.001
speed_cond_num	-0.32 (-0.47 to -0.16)	<0.001	<0.001
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 109.017 1 < 2.2e-16 speed_cond_num 15.586 1 7.885e-05 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0022, r2c: 0.9645

f2m: 0.0022, f2c: 0.0786

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,theta_avg_power

Changes in theta_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.84 (0.62 to 1.1)	<0.001	<0.001
speed_cond_num	0.38 (0.22 to 0.54)	<0.001	<0.001
subj_char.sd__(Intercept)	0.78 (NA to NA)
Residual.sd__Observation	0.37 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 57.235 1 3.867e-14 speed_cond_num 21.454 1 3.624e-06 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0151, r2c: 0.8153

f2m: 0.0153, f2c: 0.1041

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,alpha_avg_power

Changes in alpha_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.90 (0.64 to 1.2)	<0.001	<0.001
speed_cond_num	0.16 (0.00 to 0.32)	0.053	0.053
subj_char.sd__(Intercept)	0.99 (NA to NA)
Residual.sd__Observation	0.37 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 44.739 1 2.252e-11 *** speed_cond_num 3.747 1 0.0529 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0018, r2c: 0.8763

f2m: 0.0018, f2c: 0.0144

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

8,beta_avg_power

Changes in beta_avg_power for Cluster: 8
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.5 (1.2 to 1.8)	<0.001	<0.001
speed_cond_num	-0.15 (-0.25 to -0.05)	0.003	0.003
subj_char.sd__(Intercept)	1.1 (NA to NA)
Residual.sd__Observation	0.23 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 109.9469 1 < 2e-16 * speed_cond_num 8.5527 1 0.00345 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0013, r2c: 0.9587

f2m: 0.0013, f2c: 0.0383

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,theta_avg_power

Changes in theta_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.63 (0.30 to 0.96)	<0.001	<0.001
speed_cond_num	0.14 (-0.03 to 0.31)	0.10	0.10
subj_char.sd__(Intercept)	1.0 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 14.0193 1 0.0001809 *** speed_cond_num 2.6484 1 0.1036535
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0013, r2c: 0.9146

f2m: 0.0013, f2c: 0.013

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,alpha_avg_power

Changes in alpha_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.8 (3.0 to 4.6)	<0.001	<0.001
speed_cond_num	-0.41 (-0.72 to -0.10)	0.009	0.009
subj_char.sd__(Intercept)	2.7 (NA to NA)
Residual.sd__Observation	0.59 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.778 1 < 2.2e-16 * speed_cond_num 6.754 1 0.009354 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0017, r2c: 0.9553

f2m: 0.0017, f2c: 0.0439

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

9,beta_avg_power

Changes in beta_avg_power for Cluster: 9
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.4 (1.1 to 1.7)	<0.001	<0.001
speed_cond_num	-0.40 (-0.57 to -0.24)	<0.001	<0.001
subj_char.sd__(Intercept)	0.99 (NA to NA)
Residual.sd__Observation	0.30 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 80.972 1 < 2.2e-16 speed_cond_num 24.211 1 8.634e-07 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0118, r2c: 0.9146

f2m: 0.012, f2c: 0.1765

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,theta_avg_power

Changes in theta_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.48 (0.17 to 0.78)	0.002	0.002
speed_cond_num	0.56 (0.37 to 0.75)	<0.001	<0.001
subj_char.sd__(Intercept)	0.87 (NA to NA)
Residual.sd__Observation	0.33 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 9.5082 1 0.002046 ** speed_cond_num 33.4908 1 7.16e-09 *** — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0278, r2c: 0.8748

f2m: 0.0286, f2c: 0.2863

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,alpha_avg_power

Changes in alpha_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.3 (1.7 to 2.9)	<0.001	<0.001
speed_cond_num	0.11 (-0.21 to 0.42)	0.50	0.50
subj_char.sd__(Intercept)	1.7 (NA to NA)
Residual.sd__Observation	0.56 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 56.8530 1 4.696e-14 *** speed_cond_num 0.4491 1 0.5028
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 3e-04, r2c: 0.9081

f2m: 3e-04, f2c: -0.0042

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

10,beta_avg_power

Changes in beta_avg_power for Cluster: 10
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.72 (0.46 to 0.97)	<0.001	<0.001
speed_cond_num	-0.02 (-0.14 to 0.09)	0.70	0.70
subj_char.sd__(Intercept)	0.77 (NA to NA)
Residual.sd__Observation	0.20 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 29.5478 1 5.455e-08 *** speed_cond_num 0.1527 1 0.6959
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 1e-04, r2c: 0.9346

f2m: 1e-04, f2c: -0.007

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,theta_avg_power

Changes in theta_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.66 (0.45 to 0.88)	<0.001	<0.001
speed_cond_num	0.15 (0.00 to 0.31)	0.056	0.056
subj_char.sd__(Intercept)	0.67 (NA to NA)
Residual.sd__Observation	0.32 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 37.1953 1 1.069e-09 *** speed_cond_num 3.6638 1 0.05561 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0034, r2c: 0.8135

f2m: 0.0034, f2c: 0.0184

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,alpha_avg_power

Changes in alpha_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.2 (2.6 to 3.8)	<0.001	<0.001
speed_cond_num	-0.33 (-0.57 to -0.09)	0.007	0.007
subj_char.sd__(Intercept)	2.1 (NA to NA)
Residual.sd__Observation	0.49 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 105.0066 1 < 2.2e-16 * speed_cond_num 7.4046 1 0.006506 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0018, r2c: 0.9509

f2m: 0.0018, f2c: 0.0421

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

11,beta_avg_power

Changes in beta_avg_power for Cluster: 11
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.0 (0.76 to 1.3)	<0.001	<0.001
speed_cond_num	-0.28 (-0.40 to -0.16)	<0.001	<0.001
subj_char.sd__(Intercept)	0.86 (NA to NA)
Residual.sd__Observation	0.24 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 63.614 1 1.513e-15 speed_cond_num 21.845 1 2.956e-06 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0079, r2c: 0.9267

f2m: 0.0079, f2c: 0.1367

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,theta_avg_power

Changes in theta_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.46 (0.25 to 0.67)	<0.001	<0.001
speed_cond_num	0.23 (0.13 to 0.33)	<0.001	<0.001
subj_char.sd__(Intercept)	0.87 (NA to NA)
Residual.sd__Observation	0.25 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 17.896 1 2.333e-05 speed_cond_num 18.650 1 1.570e-05 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0051, r2c: 0.9227

f2m: 0.0051, f2c: 0.0832

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,alpha_avg_power

Changes in alpha_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	3.9 (3.3 to 4.4)	<0.001	<0.001
speed_cond_num	-0.23 (-0.46 to 0.01)	0.059	0.059
subj_char.sd__(Intercept)	2.4 (NA to NA)
Residual.sd__Observation	0.56 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 166.8652 1 < 2e-16 *** speed_cond_num 3.5566 1 0.05931 .
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 6e-04, r2c: 0.9484

f2m: 6e-04, f2c: 0.0122

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

12,beta_avg_power

Changes in beta_avg_power for Cluster: 12
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	2.3 (2.0 to 2.6)	<0.001	<0.001
speed_cond_num	-0.23 (-0.38 to -0.08)	0.002	0.002
subj_char.sd__(Intercept)	1.3 (NA to NA)
Residual.sd__Observation	0.35 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 198.0068 1 < 2.2e-16 * speed_cond_num 9.4525 1 0.002109 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0023, r2c: 0.9313

f2m: 0.0023, f2c: 0.0399

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,theta_avg_power

Changes in theta_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.59 (0.32 to 0.86)	<0.001	<0.001
speed_cond_num	0.28 (0.12 to 0.44)	<0.001	<0.001
subj_char.sd__(Intercept)	0.79 (NA to NA)
Residual.sd__Observation	0.28 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: theta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 18.002 1 2.207e-05 speed_cond_num 12.024 1 0.0005251 — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0088, r2c: 0.8889

f2m: 0.0089, f2c: 0.0976

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,alpha_avg_power

Changes in alpha_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	1.8 (1.2 to 2.4)	<0.001	<0.001
speed_cond_num	-0.12 (-0.34 to 0.11)	0.30	0.30
subj_char.sd__(Intercept)	1.8 (NA to NA)
Residual.sd__Observation	0.39 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: alpha_avg_power Chisq Df Pr(>Chisq)
(Intercept) 33.2238 1 8.214e-09 *** speed_cond_num 1.0684 1 0.3013
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 3e-04, r2c: 0.9557

f2m: 3e-04, f2c: 9e-04

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot

13,beta_avg_power

Changes in beta_avg_power for Cluster: 13
Characteristic	Beta (95% CI)1	p-value	q-value2
(Intercept)	0.73 (0.48 to 0.99)	<0.001	<0.001
speed_cond_num	-0.14 (-0.26 to -0.02)	0.026	0.026
subj_char.sd__(Intercept)	0.76 (NA to NA)
Residual.sd__Observation	0.22 (NA to NA)
1 CI = Confidence Interval
2 False discovery rate correction for multiple testing

Analysis of Deviance Table (Type III Wald chisquare tests)

Response: beta_avg_power Chisq Df Pr(>Chisq)
(Intercept) 31.5165 1 1.978e-08 ** speed_cond_num 4.9565 1 0.02599
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

model effect sizes

[1] “intercept model” r2m: 0.0024, r2c: 0.9257

f2m: 0.0024, f2c: 0.0352

model validations

[[1]] [[2]] [[2]]$subj_char

[[3]] [[4]]

model validations

data plot